speech text engine

13 Best Free Speech-to-Text Open Source Engines, APIs, and AI Models

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Automatic speech-to-text recognition involves converting an audio file to editable text. Computer algorithms facilitate this process in four steps: analyze the audio, break it down into parts, convert it into a computer-readable format, and use the algorithm again to match it into a text-readable format.

In the past, this was a task only reserved for proprietary systems. This was disadvantageous to the user due to high licensing and usage fees, limited features, and a lack of transparency. 

As more people researched these tools, creating your language processing models with the help of open-source voice recognition systems became possible . These systems, made by the community for the community, are easy to customize, cheap to use, and transparent, giving the user control over their data.

Best 13 Open-Source Speech Recognition Systems

An open-source speech recognition system is a library or framework consisting of the source code of a speech recognition system. These community-based projects are made available to the public under an open-source license. Users can contribute to these tools, customize them, or even tailor them to their needs.

Here are the top open-source speech recognition engines you can start on: 

Whisper is Open AI’s newest brainchild that offers transcription and translation services.  Released in September 2022, this AI tool is one of the most accurate automatic speech recognition models. It stands out from the rest of the tools in the market due to the large number of training data sets it was trained on: 680 thousand hours of audio files from the internet. This diverse range of data improves the human-level robustness of the tool.

You must install Python or the command line interface to transcribe using Whisper. Five models are available to work with; all have different sizes and capabilities. These include tiny, base, small, medium, and large. The larger the model, the faster the transcription speed. Still, you must invest in a good CPU and GPU device to maximize their use.

Whisper AI falls short compared to models proficient in LibriSpeech performance (one of the most common speech recognition benchmarks). However, its zero-shot performance reveals that the API has 50% fewer errors than the same models.

It supports content formats such as MP3, MP4, M4A, Mpeg, MPGA, WEBM, and WAV.

It can transcribe 99 languages and translate them all into English.

The tool is free to use.

The larger the model, the more GPU resources it consumes, which can be costly. 

It will cost you time and resources to install and use the tool.

It does not provide real-time transcription.

2. Project DeepSpeech

Project DeepSearch is an open-source speech-to-text engine by Mozilla. This voice-to-text command and library is released under the Mozilla Public License (MPL). Its model follows the Baidu Deep Speech research paper, making it end-to-end trainable and capable of transcribing audio in several languages. It is also trained and implemented using Google’s TensorFlow. 

Download the source code from GitHub and install it in your Python to use it. The tool comes when already pre-trained on an English model. However, you can still train the model with your data. Alternatively, you can get a pre-trained model and improve it using custom data.

DeepSpeech is easy to customize since it’s a code-native solution.

It provides special wrappers for Python, C, .Net Framework, and Javascript, allowing you to use the tool regardless of the language.

It can function on various gadgets, including a Raspberry Pi device. 

Its per-word error rate is remarkably low at 7.5%.

Mozilla takes a serious approach to privacy concerns.

Mozilla is reportedly ending the development of DeepSpeech. This means there will be less support in case of bugs and implementation problems.

Kaldi is a speech recognition tool purposely created for speech recognition researchers. It’s written in C++ and released under the Apache 2.0 license, one of the least restrictive licenses. Unlike tools like Whisper and DeepSpeech, which focus on deep learning, Kaldi primarily focuses on speech recognition models that use old-school, reliable tools. These include models like HMMs (Hidden Markov Models), GMMs (Gaussian Mixture Models), and FSTs (Finite State Transducers.)

Kaldi is very reliable. Its code is thoroughly tested and verified. 

Although its focus is not on deep learning, it has some models that can help with transcription services.

It is perfect for academic and industry-related research, allowing users to test their models and techniques.

It has an active forum that provides the right amount of support.

There are also resources and documentation available to help users address any issues.

Being open-source, users with privacy or security concerns can inspect the code to understand how it works.

Its classical approach to models may limit its accuracy levels. 

Kaldi is not user-friendly since it operates on a Command-line interface.

It's pretty complex to use, making it suitable for users with technical experience.

You need lots of computation power to use the toolkit.

4. SpeechBrain

SpeechBrain is an open-source toolkit that facilitates the research and development of speech-related tech. It supports a variety of tasks, including speech recognition, enhancement, separation, speaker diarization, and microphone signal processing. Speechbrain uses PyTorch as its foundation, taking advantage of its flexibility and ease of use. Developers and researchers can also benefit from Pytorch’s expensive ecosystem and support to build and train their neural networks.

Users can choose between both traditional and deep-leaning-based ASR models.

It's easy to customize a model to adapt to your needs. 

Its integration with Pytorch makes it easier to use.  

There are available pre-trained models users can use to get started with speech-to-text tasks.

The SpeechBrain documentation is not as extensive as that of Kaldi.

Its pre-trained models are limited.

You may need particular expertise to use the tool. Without it, you may need to undergo a steep learning curve.

Coqui is an advanced deep learning toolkit perfect for training and deploying stt models. Licensed under the Mozilla Public License 2.0, you can use it to generate multiple transcripts, each with a confidence score. It provides pre-trained models alongside example audio files you can use to test the engine and help with further fine-tuning. Moreover, it has well-detailed documentation and resources that can help you use and solve any arising problems.

The STT models it provides are highly trained with high-quality data. 

The models support multiple languages.

There is a friendly support community where you can ask questions and get any details relating to STT.

It supports real-time transcription with extremely low latency in seconds. 

Developers can customize the models to various use cases, from transcription to acting as voice assistants. 

Coqui stopped to maintain the STT project to focus on their text-to-speech toolkit. This means you may have to solve any problems that arise by yourself without any help from support.

Julius is one of the oldest speech-to-text projects, dating back to 1997, with roots in Japan. It is available under the BSD -3-license, making it accessible to developers. It strongly supports Japanese ASR, but being a language-independent program, the model can understand and process multiple languages, including English, Slovenian, French, Thai, and others. The transcription accuracy largely depends on whether you have the right language and acoustic model. The project is written in the most common language, C, allowing it to work in Windows, Linux, Android, and macOS systems.

Julius can perform real-time speech-to-text transcription with low memory usage.

It has an active community that can help with ASR problems.

The models trained in English are readily available on the web for download.

It does not need internet access for speech recognition, making it suitable for users needing privacy.

Like any other open-source program, you need users with technical experience to make it work.

It has a huge learning curve.

7. Flashlight ASR (Formerly Wav2Letter++)

Flashlight ASR is an open-source speech recognition toolkit designed by the Facebook AI research team. Its capability to handle large datasets, speed, and efficiency stands out. You can attribute the speed to using only convolutional neural networks in the language modeling, machine translation, and speech synthesis. 

Ideally, most speech recognition engines use convolutionary and recurrent neural networks to understand and model the language. However, recurrent networks may need high computation power, thus affecting the speed of the engine.

The Flashlight ASR is compiled using modern C++, an easy language on your device’s CPU and GPU. It’s also built on Flashlight, a stand-alone library for machine learning.

It's one of the fastest machine learning speech-to-text systems.

You can adapt its use to various languages and dialects.

The model does not consume a lot of GPU and CPU resources.

It does not provide any pre-trained language models, including English.

You need to have deep coding expertise to operate the tool.

It has a steep learning curve for new users.

8. PaddleSpeech (Formerly DeepSpeech2)

This open-source speech-to-text toolkit is available on the Paddlepaddle platform and provided under the Apache 2.0 license. PaddleSpeech is one of the most versatile toolkits capable of performing speech recognition, speech-to-text conversion, keyword spotting, translation, and audio classification. Its transcription quality is so good that it won the NAACL2022 Best Demo Award .

This speech-to-text engine supports various language models but prioritizes Chinese and English models. The Chinese model, in particular, features text normalization and pronunciation to make it adapt to the rules of the Chinese language.

The toolkit delivers high-end and ultra-lightweight models that use the best technology in the market.

The speech-to-text engine provides both command-line and server options, making it user-friendly to adopt.

It is very convenient for users by both developers and researchers.

Its source code is written in Python, one of the most commonly used languages.

Its focus on Chinese leads to the limitation of resources and support for other languages.

It has a steep learning curve.

You need to have certain expertise to integrate and use the tool.

9. OpenSeq2Seq

Like its name, OpenSeq2Seq is an open-source speech-to-text tool kit that helps train different types of sequence-to-sequence models. Developed by Nvidia, this toolkit is released under the Apache 2.0 license, meaning it's free for everyone. It trains language models that perform transcription, translation, automatic speech recognition, and sentiment analysis tasks.

To use it, use the default models or train your own, depending on your needs. OpenSeq2Seq performs best when you use many graphics cards and computers simultaneously. It works best on Nvidia-powered devices.

The tool has multiple functions, making it very versatile.

It can work with the most recent Python, TensorFlow, and CUDA versions. 

Developers and researchers can access the tool, collaborate, and make their innovations.

Beneficial to users with Nvidia-powered devices.

It can consume significant computer resources due to its parallel processing capability.

Community support has reduced over time as Nvidia paused the project development.

Users without access to Nvidia hardware can be at a disadvantage.

One of the most compact and lightweight speech-to-text engines today is Vosk . This open-source toolkit works offline on multiple devices, including Android, iOS, and Raspberry Pi. It supports over 20 languages and dialects, including English, Chinese, Portuguese, Polish and German.

Vosk provides users with small language models that do not take up much space. Ideally, around 50MB. However, a few large models can take up to 1.4GB. The tool is quick to respond and can convert speech to text continuously.

It can work with various programming languages such as Java, Python, C++, Kotlyn, and Shell, making it a versatile addition for developers. 

It has various use cases, from transcriptions to developing chatbots and virtual assistants. 

It has a fast response time. 

The engine's accuracy can vary depending on the language and accent.

You need coding expertise to integrate and use the tool.

Athena is another sequence-to-sequence-based speech-to-text open-source engine released under the Apache 2.0 license. This toolkit suits researchers and developers with their end-to-end speech processing needs. Some tasks the models can handle include automatic speech recognition (ASR), speech synthesis, voice detection, and keyword spotting. All the language models are implemented on TensorFlow, making the toolkit accessible to more developers.

Athena is versatile in its use, from transcription services to speech synthesis.

It does not depend on Kaldi since it has its pythonic feature extractor.

The tool is well maintained with regular updates and new features.

It is open source, free to use, and available to various users.

It has a deep learning curve for new users.

Although it has a WeChat group for community support, it limits the accessibility to only those who can access the platform.

ESPnet is an open-source speech-to-text software released under the Apache 2.0 license. It provides end-to-end speech processing capabilities that cover tasks ranging from ASR, translation, speech synthesis, enhancement, and diarization. The toolkit stands out for leveraging Pytorch as its deep learning framework and following the Kaldi data processing style. As a result, you get comprehensive recipes for various language-processing tasks. The tool is also multi-lingual as it is capable of handling various languages. Use it with the readily available pre-trained models or create your own according to your needs.

The toolkit delivers a stand-out performance compared to other speech-to-text software.

It can process audio in real time, making it suitable for live transcription services.

Suitable for use by researchers and developers.

It is one of the most versatile tools to deliver various speech-processing tasks.

It can be complex to integrate and use for new users.

You must be familiar with Pytorch and Python to run the toolkit.

13. Tensorflow ASR

Our last feature on this list of free speech-to-text open-source engines is the Tensorflow ASR . This GitHub project is released under the Apache 2.0 license and uses Tensorflow 2.0 as the deep learning framework to implement various speech processing models.

Tensorflow has an incredible accuracy rate, with the author claiming it to be an almost ‘state-of-the-art’ model. It’s also one of the most well-maintained tools that undergo regular updates to improve its functionality. For example, the toolkit now supports language training on TPUs (a special hardware).

Tensorflow also supports using specific models such as Conformer, ContextNet, DeepSpeech2, and Jasper. You can choose the tool depending on the tasks you intend to handle. For example, for general tasks, consider DeepSpeech2, but for precision, use Conformer.

The language models are accurate and highly efficient when processing speech-to-text.

You can convert the models to a TFlite format to make it lightweight and easy to deploy.

It can deliver on various speech-to-text-related tasks. 

It Supports multiple languages and provides pre-trained English, Vietnamese, and German models.

The installation process can be quite complex for beginners. Users need to have a particular expertise.

There is a learning curve to using advanced models.

TPUs do not allow testing, limiting the tool's capabilities.

Top 3 Speech-to-Text APIs and AI Models

A Speech-to-text API and AI model is a tech solution that helps users convert their speech or audio files into text. Most of these solutions are cloud-based. You need to access the internet and make an API request to use them. The decision to use either APIs, AI models, or open-source engines largely depends on your needs. An API or AI model is the most preferred for small-scale tasks that are needed quickly. However, for large-scale use, consider using an open-source engine. 

Several other differences exist between speech-to-text APIs /AI models and open-source engines. Let's take a look at the most common in the table below:

After considerable research, here are our top three speech-to-text API and AI models:

The Google Cloud Speech-to-text API is one of the most common speech recognition technologies for developers looking to integrate the service into their applications. It automatically detects and converts audio to text using neural network models. Initially, the purpose of this toolkit was for use on Google’s home voice assistant, as its focus is on short command and response applications. Although the accuracy level is not that high, it does an excellent job of transcribing with minimal errors. However, the quality of the transcript is dependent on the audio quality.

Google Cloud speech-to-text API uses a pay-as-you-go subscription, priced according to the number of audio files processed per month measured per second. Users get 60 free transcription minutes plus Google Cloud hosting credits worth $300 for the first 90 days. Any audio over 60 minutes will cost you an additional $0.006 per 15 seconds.

The API can transcribe more than 125 languages and variants.

You can deploy the tool in the cloud and on-premise.

It provides automatic language transcription and translation services.

You can configure it to transcribe your phone and video conversations.

It is not free to use.

It has a limited vocabulary builder.

2. AWS Transcribe

AWS transcribe is an on-demand voice-to-text API allowing users to generate audio transcriptions. If you have heard of the Alexa voice assistant, it's the tool behind the development. Unlike every other consumer-oriented transcription tool, the AWS API has a daily good accuracy level. It can also distinguish voices in a conversation and provide timestamps to the transcript. This tool supports 37 languages, including English, German, Hebrew, Japanese, and Turkish.

Integrating it into an existing AWS ecosystem is effortless.

It is one of the best short audio commands and response options.

It is highly scalable.

It has a reasonably good accuracy level.

It is expensive to use.

It only supports cloud deployment.

It has limited support.

The tool can be slow at times.

3. AssemblyAI

AssemblyAI API is one of the best solutions for users looking to transcribe speech without many technical terms, jargon, or accents. This API model automatically detects audio, transcribes it, and even creates a summary. It also provides services such as speaker diarization, sentiment analysis, topic detection, content moderation, and entity detection.

AssemblyAI has a simple and open pricing model, where you pay for only what you use. For example, you may need to pay $0.650016 per hour to get the core transcription service, while real-time transcription costs $0.75024 per hour.

It is not expensive to use.

Accuracy levels are high for not-technical languages.

It provides helpful documentation.

The toolkit is easy to set up, even for beginners.

Its deployment speed is slow.

Its accuracy levels drop when dealing with technical terms.

What is the Best Open Source Speech Recognition System?

As you can see above, every tool from this list has benefits and disadvantages. Choosing the best open-source speech recognition system depends on your needs and available resources. For example, if you are looking for a lightweight toolkit compatible with almost every device, Voskand Julius beat the rest of the tools in this list. You can use them on Android, iOS, and even Raspberry Pi. Moreover, they don’t consume much space.

For users who want to train their models, you can use toolkits such as Whisper, OpenSeq2Seq, Flashlight ASR, and Athena.

The best approach to choosing an open-source voice recognition software is to review its documentation to understand the necessary resources and test it to see if it works for your case.

Introducing the Notta AI Model 

As shown above, AI models differ from open-source engines. They are fast, more efficient, easy to use, and can deliver high accuracy. Moreover, their use is not only limited to users with experience. Anyone can operate the tools and generate transcripts in minutes. 

Here is where we come in. Notta is one of the leading speech-to-text AI models that can transcribe and summarize your audio and video recordings. This AI tool supports 58 languages and can deliver transcripts with an impressive accuracy rate of 98.86%. The tool is available for use both on mobile and web.

Notta is easy to set up and use.

It supports multiple video and audio formats.

Its transcription speed is lightning-fast.

It adopts rigorous security protocols to protect user data.

It's free to use.

There is a limit to the file size you can upload to transcribe.

The free version supports only a limited number of transcriptions per month.

The advancement of speech recognition technology has been impressive over the years. What was once a world of proprietary software has shifted to one led by open-source toolkits and APIs/AI.

It's too early to say which is the clear winner, as they are all improving. You can, however, take advantage of their services, which include transcription, translation, dictation, speech synthesis, keyword spotting, diarization, and language enhancement.

There is no right or wrong tool in the options above. Every one of them has its strengths and weaknesses. Carefully assess your needs and resources before choosing a tool to make an informed decision.

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The top free Speech-to-Text APIs, AI Models, and Open Source Engines

This post compares the best free Speech-to-Text APIs and AI models on the market today, including APIs that have a free tier. We’ll also look at several free open-source Speech-to-Text engines and explore why you might choose an API vs. an open-source library, or vice versa.

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Choosing the best Speech-to-Text API , AI model, or open-source engine to build with can be challenging. You need to compare accuracy, model design, features, support options, documentation, security, and more.

This post examines the best free Speech-to-Text APIs and AI models on the market today, including ones that have a free tier, to help you make an informed decision. We’ll also look at several free open-source Speech-to-Text engines and explore why you might choose an API or AI model vs. an open-source library, or vice versa.

Looking for a powerful speech-to-text API or AI model?

Learn why AssemblyAI is the leading Speech AI partner.

Free Speech-to-Text APIs and AI Models

APIs and AI models are more accurate, easier to integrate, and come with more out-of-the-box features than open-source options. However, large-scale use of APIs and AI models can come with a higher cost than open-source options.

If you’re looking to use an API or AI model for a small project or a trial run, many of today’s Speech-to-Text APIs and AI models have a free tier. This means that the API or model is free for anyone to use up to a certain volume per day, per month, or per year.

Let’s compare three of the most popular Speech-to-Text APIs and AI models with a free tier: AssemblyAI, Google, and AWS Transcribe.

AssemblyAI is an API platform that offers AI models that accurately transcribe and understand speech, and enable users to extract insights from voice data. AssemblyAI offers cutting-edge AI models such as Speaker Diarization , Topic Detection, Entity Detection , Automated Punctuation and Casing , Content Moderation , Sentiment Analysis , Text Summarization , and more. These AI models help users get more out of voice data, with continuous improvements being made to accuracy .

AssemblyAI also offers LeMUR , which enables users to leverage Large Language Models (LLMs) to pull valuable information from their voice data—including answering questions, generating summaries and action items, and more. 

The company offers up to 100 free transcription hours for audio files or video streams, with a concurrency limit of 5, before transitioning to an affordable paid tier.

Its high accuracy and diverse collection of AI models built by AI experts make AssemblyAI a sound option for developers looking for a free Speech-to-Text API. The API also supports virtually every audio and video file format out-of-the-box for easier transcription.

AssemblyAI has expanded the languages it supports to include English, Spanish, French, German, Japanese, Korean, and much more, with additional languages being released monthly. See the full list here .

AssemblyAI’s easy-to-use models also allow for quick set-up and transcription in any programming language. You can copy/paste code examples in your preferred language directly from the AssemblyAI Docs or use the AssemblyAI Python SDK or another one of its ready-to-use integrations .

• Free to test in the AI playground , plus 100 free hours of asynchronous transcription with an API sign-up
• Speech-to-Text – $0.37 per hour
• Real-time Transcription – $0.47 per hour
• Audio Intelligence – varies, $.01 to $.15 per hour
• LeMUR – varies
• Enterprise pricing is also available

See the full pricing list here .

• High accuracy
• Breadth of AI models available, built by AI experts
• Continuous model iteration and improvement
• Developer-friendly documentation and SDKs
• Enterprise-grade support and security
• Models are not open-source

Google Speech-to-Text is a well-known speech transcription API. Google gives users 60 minutes of free transcription, with $300 in free credits for Google Cloud hosting.

Google only supports transcribing files already in a Google Cloud Bucket, so the free credits won’t get you very far. Google also requires you to sign up for a GCP account and project — whether you're using the free tier or paid.

With good accuracy and 125+ languages supported, Google is a decent choice if you’re willing to put in some initial work.

• 60 minutes of free transcription
• $300 in free credits for Google Cloud hosting
• Decent accuracy
• Multi-language support
• Only supports transcription of files in a Google Cloud Bucket
• Difficult to get started
• Lower accuracy than other similarly-priced APIs
• AWS Transcribe

AWS Transcribe offers one hour free per month for the first 12 months of use.

Like Google, you must create an AWS account first if you don’t already have one. AWS also has lower accuracy compared to alternative APIs and only supports transcribing files already in an Amazon S3 bucket.

However, if you’re looking for a specific feature, like medical transcription, AWS has some options. Its Transcribe Medical API is a medical-focused ASR option that is available today.

• One hour free per month for the first 12 months of use
• Tiered pricing , based on usage, ranges from $0.02400 to $0.00780
• Integrates into existing AWS ecosystem
• Medical language transcription
• Difficult to get started from scratch
• Only supports transcribing files already in an Amazon S3 bucket

Open-Source Speech Transcription engines

An alternative to APIs and AI models, open-source Speech-to-Text libraries are completely free--with no limits on use. Some developers also see data security as a plus, since your data doesn’t have to be sent to a third party or the cloud.

There is work involved with open-source engines, so you must be comfortable putting in a lot of time and effort to get the results you want, especially if you are trying to use these libraries at scale. Open-source Speech-to-Text engines are typically less accurate than the APIs discussed above.

If you want to go the open-source route, here are some options worth exploring:

DeepSpeech is an open-source embedded Speech-to-Text engine designed to run in real-time on a range of devices, from high-powered GPUs to a Raspberry Pi 4. The DeepSpeech library uses end-to-end model architecture pioneered by Baidu.

DeepSpeech also has decent out-of-the-box accuracy for an open-source option and is easy to fine-tune and train on your own data.

• Easy to customize
• Can use it to train your own model
• Can be used on a wide range of devices
• Lack of support
• No model improvement outside of individual custom training
• Heavy lift to integrate into production-ready applications

Kaldi is a speech recognition toolkit that has been widely popular in the research community for many years.

Like DeepSpeech, Kaldi has good out-of-the-box accuracy and supports the ability to train your own models. It’s also been thoroughly tested—a lot of companies currently use Kaldi in production and have used it for a while—making more developers confident in its application.

• Can use it to train your own models
• Active user base
• Can be complex and expensive to use
• Uses a command-line interface

Flashlight ASR (formerly Wav2Letter)

Flashlight ASR, formerly Wav2Letter, is Facebook AI Research’s Automatic Speech Recognition (ASR) Toolkit. It is also written in C++ and usesthe ArrayFire tensor library.

Like DeepSpeech, Flashlight ASR is decently accurate for an open-source library and is easy to work with on a small project.

• Customizable
• Easier to modify than other open-source options
• Processing speed
• Very complex to use
• No pre-trained libraries available
• Need to continuously source datasets for training and model updates, which can be difficult and costly
• SpeechBrain

SpeechBrain is a PyTorch-based transcription toolkit. The platform releases open implementations of popular research works and offers a tight integration with Hugging Face for easy access.

Overall, the platform is well-defined and constantly updated, making it a straightforward tool for training and finetuning.

• Integration with Pytorch and Hugging Face
• Pre-trained models are available
• Supports a variety of tasks
• Even its pre-trained models take a lot of customization to make them usable
• Lack of extensive docs makes it not as user-friendly, except for those with extensive experience

Coqui is another deep learning toolkit for Speech-to-Text transcription. Coqui is used in over twenty languages for projects and also offers a variety of essential inference and productionization features.

The platform also releases custom-trained models and has bindings for various programming languages for easier deployment.

• Generates confidence scores for transcripts
• Large support comunity
• No longer updated and maintained by Coqui

Whisper by OpenAI, released in September 2022, is comparable to other current state-of-the-art open-source options.

Whisper can be used either in Python or from the command line and can also be used for multilingual translation.

Whisper has five different models of varying sizes and capabilities, depending on the use case, including v3 released in November 2023 .

However, you’ll need a fairly large computing power and access to an in-house team to maintain, scale, update, and monitor the model to run Whisper at a large scale, making the total cost of ownership higher compared to other options. 

As of March 2023, Whisper is also now available via API . On-demand pricing starts at $0.006/minute.

• Multilingual transcription
• Can be used in Python
• Five models are available, each with different sizes and capabilities
• Need an in-house research team to maintain and update
• Costly to run

Which free Speech-to-Text API, AI model, or Open Source engine is right for your project?

The best free Speech-to-Text API, AI model, or open-source engine will depend on our project. Do you want something that is easy-to-use, has high accuracy, and has additional out-of-the-box features? If so, one of these APIs might be right for you:

Alternatively, you might want a completely free option with no data limits—if you don’t mind the extra work it will take to tailor a toolkit to your needs. If so, you might choose one of these open-source libraries:

Whichever you choose, make sure you find a product that can continually meet the needs of your project now and what your project may develop into in the future.

Want to get started with an API?

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Best text-to-speech software of 2024

Boosting accessibility and productivity

• Best overall
• Best realism
• Best for developers
• Best for podcasting
• How we test

The best text-to-speech software makes it simple and easy to convert text to voice for accessibility or for productivity applications.

1. Best overall 2. Best realism 3. Best for developers 4. Best for podcasting 5. Best for developers 6. FAQs 7. How we test

Finding the best text-to-speech software is key for anyone looking to transform written text into spoken words, whether for accessibility purposes, productivity enhancement, or creative applications like voice-overs in videos. 

Text-to-speech (TTS) technology relies on sophisticated algorithms to model natural language to bring written words to life, making it easier to catch typos or nuances in written content when it's read aloud. So, unlike the best speech-to-text apps and best dictation software , which focus on converting spoken words into text, TTS software specializes in the reverse process: turning text documents into audio. This technology is not only efficient but also comes with a variety of tools and features. For those creating content for platforms like YouTube , the ability to download audio files is a particularly valuable feature of the best text-to-speech software.

While some standard office programs like Microsoft Word and Google Docs offer basic TTS tools, they often lack the comprehensive functionalities found in dedicated TTS software. These basic tools may provide decent accuracy and basic options like different accents and languages, but they fall short in delivering the full spectrum of capabilities available in specialized TTS software.

To help you find the best text-to-speech software for your specific needs, TechRadar Pro has rigorously tested various software options, evaluating them based on user experience, performance, output quality, and pricing. This includes examining the best free text-to-speech software as well, since many free options are perfect for most users. We've brought together our picks below to help you choose the most suitable tool for your specific needs, whether for personal use, professional projects, or accessibility requirements.

The best text-to-speech software of 2024 in full:

Why you can trust TechRadar We spend hours testing every product or service we review, so you can be sure you’re buying the best. Find out more about how we test.

Below you'll find full write-ups for each of the entries on our best text-to-speech software list. We've tested each one extensively, so you can be sure that our recommendations can be trusted.

The best text-to-speech software overall

1. NaturalReader

Our expert review:

Reasons to buy

Reasons to avoid.

If you’re looking for a cloud-based speech synthesis application, you should definitely check out NaturalReader. Aimed more at personal use, the solution allows you to convert written text such as Word and PDF documents, ebooks and web pages into human-like speech.  

Because the software is underpinned by cloud technology, you’re able to access it from wherever you go via a smartphone, tablet or computer. And just like Capti Voice, you can upload documents from cloud storage lockers such as Google Drive, Dropbox and OneDrive.  

Currently, you can access 56 natural-sounding voices in nine different languages, including American English, British English, French, Spanish, German, Swedish, Italian, Portuguese and Dutch. The software supports PDF, TXT, DOC(X), ODT, PNG, JPG, plus non-DRM EPUB files and much more, along with MP3 audio streams. 

There are three different products: online, software, and commercial. Both the online and software products have a free tier.

Read our full NaturalReader review .

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The best text-to-speech software for realistic voices

Specializing in voice synthesis technology, Murf uses AI to generate realistic voiceovers for a range of uses, from e-learning to corporate presentations. 

Murf comes with a comprehensive suite of AI tools that are easy to use and straightforward to locate and access. There's even a Voice Changer feature that allows you to record something before it is transformed into an AI-generated voice- perfect if you don't think you have the right tone or accent for a piece of audio content but would rather not enlist the help of a voice actor. Other features include Voice Editing, Time Syncing, and a Grammar Assistant.

The solution comes with three pricing plans to choose from: Basic, Pro and Enterprise. The latter of these options may be pricey but some with added collaboration and account management features that larger companies may need access to. The Basic plan starts at around $19 / £17 / AU$28 per month but if you set up a yearly plan that will drop to around $13 / £12 / AU$20 per month. You can also try the service out for free for up to 10 minutes, without downloads.

The best text-to-speech software for developers

3. Amazon Polly

Alexa isn’t the only artificial intelligence tool created by tech giant Amazon as it also offers an intelligent text-to-speech system called Amazon Polly. Employing advanced deep learning techniques, the software turns text into lifelike speech. Developers can use the software to create speech-enabled products and apps. 

It sports an API that lets you easily integrate speech synthesis capabilities into ebooks, articles and other media. What’s great is that Polly is so easy to use. To get text converted into speech, you just have to send it through the API, and it’ll send an audio stream straight back to your application. 

You can also store audio streams as MP3, Vorbis and PCM file formats, and there’s support for a range of international languages and dialects. These include British English, American English, Australian English, French, German, Italian, Spanish, Dutch, Danish and Russian. 

Polly is available as an API on its own, as well as a feature of the AWS Management Console and command-line interface. In terms of pricing, you’re charged based on the number of text characters you convert into speech. This is charged at approximately $16 per1 million characters , but there is a free tier for the first year.

The best text-to-speech software for podcasting

In terms of its library of voice options, it's hard to beat Play.ht as one of the best text-to-speech software tools. With almost 600 AI-generated voices available in over 60 languages, it's likely you'll be able to find a voice to suit your needs. 

Although the platform isn't the easiest to use, there is a detailed video tutorial to help users if they encounter any difficulties. All the usual features are available, including Voice Generation and Audio Analytics. 

In terms of pricing, Play.ht comes with four plans: Personal, Professional, Growth, and Business. These range widely in price, but it depends if you need things like commercial rights and affects the number of words you can generate each month. 

The best text-to-speech software for Mac and iOS

5. Voice Dream Reader

There are also plenty of great text-to-speech applications available for mobile devices, and Voice Dream Reader is an excellent example. It can convert documents, web articles and ebooks into natural-sounding speech. 

The app comes with 186 built-in voices across 30 languages, including English, Arabic, Bulgarian, Catalan, Croatian, Czech, Danish, Dutch, Finnish, French, German, Greek, Hebrew, Hungarian, Italian, Japanese and Korean. 

You can get the software to read a list of articles while you drive, work or exercise, and there are auto-scrolling, full-screen and distraction-free modes to help you focus. Voice Dream Reader can be used with cloud solutions like Dropbox, Google Drive, iCloud Drive, Pocket, Instapaper and Evernote. 

The best text-to-speech software: FAQs

What is the best text-to-speech software for youtube.

If you're looking for the best text-to-speech software for YouTube videos or other social media platforms, you need a tool that lets you extract the audio file once your text document has been processed. Thankfully, that's most of them. So, the real trick is to select a TTS app that features a bountiful choice of natural-sounding voices that match the personality of your channel. 

What’s the difference between web TTS services and TTS software?

Web TTS services are hosted on a company or developer website. You’ll only be able to access the service if the service remains available at the whim of a provider or isn’t facing an outage.

TTS software refers to downloadable desktop applications that typically won’t rely on connection to a server, meaning that so long as you preserve the installer, you should be able to use the software long after it stops being provided. 

Do I need a text-to-speech subscription?

Subscriptions are by far the most common pricing model for top text-to-speech software. By offering subscription models for, companies and developers benefit from a more sustainable revenue stream than they do from simply offering a one-time purchase model. Subscription models are also attractive to text-to-speech software providers as they tend to be more effective at defeating piracy.

Free software options are very rarely absolutely free. In some cases, individual voices may be priced and sold individually once the application has been installed or an account has been created on the web service.

How can I incorporate text-to-speech as part of my business tech stack?

Some of the text-to-speech software that we’ve chosen come with business plans, offering features such as additional usage allowances and the ability to have a shared workspace for documents. Other than that, services such as Amazon Polly are available as an API for more direct integration with business workflows.

Small businesses may find consumer-level subscription plans for text-to-speech software to be adequate, but it’s worth mentioning that only business plans usually come with the universal right to use any files or audio created for commercial use.

How to choose the best text-to-speech software

When deciding which text-to-speech software is best for you, it depends on a number of factors and preferences. For example, whether you’re happy to join the ecosystem of big companies like Amazon in exchange for quality assurance, if you prefer realistic voices, and how much budget you’re playing with. It’s worth noting that the paid services we recommend, while reliable, are often subscription services, with software hosted via websites, rather than one-time purchase desktop apps. 

Also, remember that the latest versions of Microsoft Word and Google Docs feature basic text-to-speech as standard, as well as most popular browsers. So, if you have access to that software and all you’re looking for is a quick fix, that may suit your needs well enough. 

How we test the best text-to-speech software

We test for various use cases, including suitability for use with accessibility issues, such as visual impairment, and for multi-tasking. Both of these require easy access and near instantaneous processing. Where possible, we look for integration across the entirety of an operating system , and for fair usage allowances across free and paid subscription models.

At a minimum, we expect an intuitive interface and intuitive software. We like bells and whistles such as realistic voices, but we also appreciate that there is a place for products that simply get the job done. Here, the question that we ask can be as simple as “does this piece of software do what it's expected to do when asked?”

Read more on how we test, rate, and review products on TechRadar .

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John (He/Him) is the Components Editor here at TechRadar and he is also a programmer, gamer, activist, and Brooklyn College alum currently living in Brooklyn, NY. 

Named by the CTA as a CES 2020 Media Trailblazer for his science and technology reporting, John specializes in all areas of computer science, including industry news, hardware reviews, PC gaming, as well as general science writing and the social impact of the tech industry.

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Text to speech

An AI Speech feature that converts text to lifelike speech.

Bring your apps to life with natural-sounding voices

Build apps and services that speak naturally. Differentiate your brand with a customized, realistic voice generator, and access voices with different speaking styles and emotional tones to fit your use case—from text readers and talkers to customer support chatbots.

Lifelike synthesized speech

Enable fluid, natural-sounding text to speech that matches the intonation and emotion of human voices.

Customizable text-talker voices

Create a unique AI voice generator that reflects your brand's identity.

Fine-grained text-to-talk audio controls

Tune voice output for your scenarios by easily adjusting rate, pitch, pronunciation, pauses, and more.

Flexible deployment

Run Text to Speech anywhere—in the cloud, on-premises, or at the edge in containers.

Tailor your speech output

Fine-tune synthesized speech audio to fit your scenario.  Define lexicons  and control speech parameters such as pronunciation, pitch, rate, pauses, and intonation with  Speech Synthesis Markup Language  (SSML) or with the  audio content creation tool .

Deploy Text to Speech anywhere, from the cloud to the edge

Run Text to Speech wherever your data resides. Build lifelike speech synthesis into applications optimized for both robust cloud capabilities and edge locality using  containers .

Build a custom voice for your brand

Differentiate your brand with a unique  custom voice . Develop a highly realistic voice for more natural conversational interfaces using the Custom Neural Voice capability, starting with 30 minutes of audio.

Fuel App Innovation with Cloud AI Services

Learn five key ways your organization can get started with AI to realize value quickly.

Comprehensive privacy and security

Documentation.

AI Speech, part of Azure AI Services, is  certified  by SOC, FedRAMP, PCI DSS, HIPAA, HITECH, and ISO.

View and delete your custom voice data and synthesized speech models at any time. Your data is encrypted while it’s in storage.

Your data remains yours. Your text data isn't stored during data processing or audio voice generation.

Backed by Azure infrastructure, AI Speech offers enterprise-grade security, availability, compliance, and manageability.

Comprehensive security and compliance, built in

Microsoft invests more than $1 billion annually on cybersecurity research and development.

We employ more than 3,500 security experts who are dedicated to data security and privacy.

Azure has more certifications than any other cloud provider. View the comprehensive list .

Flexible pricing gives you the power and control you need

Pay only for what you use, with no upfront costs. With Text to Speech, you pay as you go based on the number of characters you convert to audio.

Get started with an Azure free account

After your credit, move to  pay as you go  to keep building with the same free services. Pay only if you use more than your free monthly amounts.

Guidelines for building responsible synthetic voices

Learn about responsible deployment

Synthetic voices must be designed to earn the trust of others. Learn the principles of building synthesized voices that create confidence in your company and services.

Obtain consent from voice talent

Help voice talent understand how neural text-to-speech (TTS) works and get information on recommended use cases.

Be transparent

Transparency is foundational to responsible use of computer voice generators and synthetic voices. Help ensure that users understand when they’re hearing a synthetic voice and that voice talent is aware of how their voice will be used. Learn more with our disclosure design guidelines.

Documentation and resources

Get started.

Read the  documentation

Take the  Microsoft Learn course

Get started with a 30-day learning journey

Explore code samples

Check out the  sample code

See customization resources

Customize your speech solution with  Speech studio . No code required.

Start building with AI Services

DeepSpeech 0.6: Mozilla’s Speech-to-Text Engine Gets Fast, Lean, and Ubiquitous

The Machine Learning team at Mozilla continues work on DeepSpeech, an automatic speech recognition (ASR) engine which aims to make speech recognition technology and trained models openly available to developers. DeepSpeech is a deep learning-based ASR engine with a simple API. We also provide pre-trained English models.

Our latest release, version v0.6, offers the highest quality, most feature-packed model so far. In this overview, we’ll show how DeepSpeech can transform your applications by enabling client-side, low-latency, and privacy-preserving speech recognition capabilities.

Consistent low latency

DeepSpeech v0.6 includes a host of performance optimizations, designed to make it easier for application developers to use the engine without having to fine tune their systems. Our new streaming decoder offers the largest improvement, which means DeepSpeech now offers consistent low latency and memory utilization, regardless of the length of the audio being transcribed. Application developers can obtain partial transcripts without worrying about big latency spikes.

DeepSpeech is composed of two main subsystems: an acoustic model and a decoder. The acoustic model is a deep neural network that receives audio features as inputs, and outputs character probabilities. The decoder uses a beam search algorithm to transform the character probabilities into textual transcripts that are then returned by the system.

In a previous blog post , I discussed how we made the acoustic model streamable. With both systems now capable of streaming, there’s no longer any need for carefully tuned silence detection algorithms in applications. dabinat , a long-term volunteer contributor to the DeepSpeech code base, contributed this feature. Thanks!

In the following diagram, you can see the same audio file being processed in real time by DeepSpeech, before and after the decoder optimizations. The program requests an intermediate transcription roughly every second while the audio is being transcribed. The dotted black line marks when the program has received the final transcription. Then, the distance from the end of the audio signal to the dotted line represents how long a user must wait after they’ve stopped speaking until the final transcript is computed and the application is able to respond.

In this case, the latest version of DeepSpeech provides the transcription 260ms after the end of the audio, which is 73% faster than before the streaming decoder was implemented. This difference would be even larger for a longer recording. The intermediate transcript requests at seconds 2 and 3 of the audio file are also returned in a fraction of the time.

Maintaining low latency is crucial for keeping users engaged and satisfied with your application. DeepSpeech enables low-latency speech recognition services regardless of network conditions, as it can run offline, on users’ devices.

TensorFlow Lite, smaller models, faster start-up times

We have added support for TensorFlow Lite , a version of TensorFlow that’s optimized for mobile and embedded devices. This has reduced the DeepSpeech package size from 98 MB to 3.7 MB. It has reduced our English model size from 188 MB to 47 MB. We did this via post-training quantization , a technique to compress model weights after training is done. TensorFlow Lite is designed for mobile and embedded devices, but we found that for DeepSpeech it is even faster on desktop platforms. And so, we’ve made it available on Windows, macOS, and Linux as well as Raspberry Pi and Android. DeepSpeech v0.6 with TensorFlow Lite runs faster than real time on a single core of a Raspberry Pi 4.

The following diagram compares the start-up time and peak memory utilization for DeepSpeech versions v0.4.1, v0.5.1, and our latest release, v0.6.0.

We now use 22 times less memory and start up over 500 times faster . Together with the optimizations we’ve applied to our language model, a complete DeepSpeech package including the inference code and a trained English model is now more than 50% smaller .

Confidence value and timing metadata in the API

In addition, the new decoder exposes timing and confidence metadata, providing new possibilities for applications. We now offer an extended set of functions in the API, not just the textual transcript. You also get metadata timing information for each character in the transcript, and a per-sentence confidence value.

The example below shows the timing metadata extracted from DeepSpeech from a sample audio file. The per-character timing returned by the API is grouped into word timings. You can see the waveform below. Click the “Play” button to listen to the sample.

Te Hiku Media are using DeepSpeech to develop and deploy the first Te reo Māori automatic speech recognizer. They have been exploring the use of the confidence metadata in our new decoder to build a digital pronunciation helper for Te reo Māori. Recently, they received a $13 million NZD investment from New Zealand’s Strategic Science Investment Fund to build Papa Reo, a multilingual language platform . They are starting with New Zealand English and Te reo Māori.

Windows/.NET support

DeepSpeech v0.6 now offers packages for Windows, with .NET, Python, JavaScript, and C bindings. Windows support was a much-requested feature that was contributed by Carlos Fonseca , who also wrote the .NET bindings and examples. Thanks Carlos!

You can find more details about our Windows support by looking at the WPF example (pictured below). It uses the .NET bindings to create a small UI around DeepSpeech. Our .NET package is available in the NuGet Gallery . You can install it directly from Visual Studio.

You can see the WPF example that’s available in our repository. It contains code demonstrating transcription from an audio file, and also from a microphone or other audio input device.

Centralized documentation

We have centralized the documentation for all our language bindings in a single website, deepspeech.readthedocs.io . You can find the documentation for C, Python, .NET, Java and NodeJS/Electron packages. Given the variety of language bindings available, we wanted to make it easier to locate the correct documentation for your platform.

Improvements for training models

With the upgrade to TensorFlow 1.14, we now leverage the CuDNN RNN APIs for our training code. This change gives us around 2x faster training times, which means faster experimentation and better models.

Along with faster training, we now also support online feature augmentation, as described in Google’s SpecAugment paper . This feature was contributed by Iara Health , a Brazilian startup providing transcription services for health professionals. Iara Health has used online augmentation to improve their production DeepSpeech models.

The video above shows a customer using the Iara Health system. By using voice commands and dictation, the user instructs the program to load a template. Then, while looking at results of an MRI scan, they dictate their findings. The user can complete the report without typing. Iara Health has trained their own Brazilian Portuguese models for this specialized use case.

Finally, we have also removed all remaining points where we assumed a known sample rate of 16kHz. DeepSpeech is now fully capable of training and deploying models at different sample rates. For example, you can now more easily train and use DeepSpeech models with telephony data, which is typically recorded at 8kHz.

Try out DeepSpeech v0.6

The DeepSpeech v0.6 release includes our speech recognition engine as well as a trained English model. We provide binaries for six platforms and, as mentioned above, have bindings to various programming languages, including Python, JavaScript, Go, Java, and .NET.

The included English model was trained on 3816 hours of transcribed audio coming from Common Voice English , LibriSpeech , Fisher , Switchboard . The model also includes around 1700 hours of transcribed WAMU (NPR) radio shows. It achieves a 7.5% word error rate on the LibriSpeech test clean benchmark, and is faster than real time on a single core of a Raspberry Pi 4.

DeepSpeech v0.6 includes our best English model yet. However, most of the data used to train it is American English. For this reason, it doesn’t perform as well as it could on other English dialects and accents. A lack of publicly available voice data in other languages and dialects is part of why Common Voice was created. We want to build a future where a speaker of Welsh or Basque or Scottish English has access to speech technology with the same standard of quality as is currently available for speakers of languages with big markets like American English, German, or Mandarin.

Want to participate in Common Voice? You can donate your voice by reading small text fragments. Or validate existing recordings in 40 different languages, with more to come. Currently, Common Voice represents the world’s largest public domain transcribed voice dataset . The dataset consists of nearly 2,400 hours of voice data with 29 languages represented, including English, French, German, Spanish and Mandarin Chinese, but also for example Welsh and Kabyle.

The v0.6 release is now available on GitHub as well as on your favorite package manager. You can download our pre-trained model and start using DeepSpeech in minutes. If you’d like to know more, you can find detailed release notes in the GitHub release ; installation and usage explanations in our README . If that doesn’t cover what you’re looking for, you can also use our discussion forum .

Reuben Morais is a Senior Research Engineer working on the Machine Learning team at Mozilla. He is currently focused on bridging the gap between machine learning research and real world applications, bringing privacy preserving speech technologies to users.

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19 comments

Hi, this looks really awesome. Is there somewhere an online demo of the new version?

We don’t have an online demo, as the focus has been on client-side recognition. We experimented with some options to run it on the browser but the technology wasn’t there yet.

Have you experimented with tensorflow.js or WebAssembly? Wasm has experimental support for threads and SIMD in some browsers. https://github.com/mozilla/DeepSpeech/issues/2233

We tried it a long time ago but it was still very rough, we couldn’t get anything working. I should take a look at it some time.

Would really want to see this! Thanks for all the awesome work you do!

Hey, thanks a lot for doing this! Your git repo lists cuda as the only GPU backend. AFAIK there is also an AMD version for tensorflow and it seems to work quite well ( people claim a Radeon VII being about as fast as 2080ti c.f. https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/issues/362 ). Did you have the chance to test it with DeepSpeech?

We don’t explicitly target CUDA, it’s just a consequence of using TensorFlow. In addition, our native client is optimized for low latency. The use case optimized for is the software running locally on the user’s machine and transcribing a single stream of audio (likely from a microphone) while it’s being recorded. Our model is already faster than real time on CPUs, so there’s no need to do extensive GPU optimization. We build and publish GPU packages so people can experiment and so we don’t accidentally break GPU support, but there’s no major optimization push happening there.

Hello Reuben Morais. Tell me where you can read in detail about the principles of recognition on which Deep Speech is based. Maybe there is a video where it is told in detail in steps. For example, I am developing my own project for voice recognition on a small microcontroller with 16kB RAM – ERS VCRS. And in my video everything is shown from beginning to end.

DeepSpeech is not applicable to that hardware, the model is too big for 16kB of RAM. You can read more about it here: https://arxiv.org/abs/1412.5567

When you speak about client-side capabilities it’s not yet runnable client side on javascript in webbrowsers, right?

We’re working towards Firefox integration, but nothing concrete to share yet. People have deployed it client-side interacting with a web front-end, but currently it requires an additional component running on the machine.

hi, i’m really glad to see a graphical interface being built so also less technical users can start using deepSpeech (as opposed to google and apple products etc). however, even after 3 hours of googling and trying out, i couldn’t understand how to make the DeepSpeechWPF run. i found this code https://deepspeech.readthedocs.io/en/v0.6.0/DotNet-contrib-examples.html and this repo https://github.com/mozilla/DeepSpeech/tree/v0.6.0/examples/net_framework/DeepSpeechWPF but PLEASE, publish some instructions that are understandable to less technical users, as i am assuming that we are who need the graphical interface most. best wishes ida

Hello, The WPF example is not meant for less technical users, it’s meant for Windows developers to have an example that uses frameworks they’re familiar with. I don’t know of any graphical interfaces for DeepSpeech that target less technical users. It’d be good to have something like that, I agree.

My primary interest in DeepSpeech is to use it in an open source home automation system that doesn’t require my voice data to leave my local network / create potential security issues. Have you done anything with DeepSpeech to integrate it into programs like MQTT? Since various open source solutions can easily use MQTT as a gateway into multiple other systems, I am wondering if there is any intentions of trying to create a simple interface between DeepSpeech and MQTT.

I don’t know of any MQTT integration.

The integration may not be all that hard if an intermediate application was written to take the output of DeepSpeech and piped it into MQTT. Might even be able to work that one out myself. Is there any way to have DeepSpeech listen to you without the need for converting it to an audio file first? Along the lines of Alexa with a key word to trigger it. Audio files just add another lay of complexity on the input side that make using DeepSpeech less useful than some of the cloud solutions.

DeepSpeech has no dependency on audio files. The API receives audio samples as input, they can come from a file or a microphone or a network stream, we don’t care.

Comments are closed for this article.

Speech engine: The technology behind text to speech

Looking for our  Text to Speech Reader ?

Featured In

Table of contents, unveiling the power of tts engines: why speechify reigns supreme, the multifaceted functionality of tts engines, why speechify stands out as the best tts platform, speechify vs. other tts engines.

Uncover the fascinating technology behind speech engines and text to speech, from how they work to the best pick.

In the digital age, the Text-to-Speech (TTS) engine has emerged as a revolutionary technology that transforms written text into natural-sounding speech. TTS engines find applications across a wide spectrum, from accessibility features to audiobooks and beyond. While many TTS platforms and engines are available, Speechify stands out as the best TTS platform, redefining the user experience with its high-quality, natural-sounding voices and an array of functionalities that cater to diverse use cases.

Understanding the TTS Engine

A Text-to-Speech engine is the heart and soul of any TTS platform, responsible for converting written text into spoken words. It utilizes advanced algorithms, machine learning, and speech synthesis techniques to ensure the output is not only understandable but also natural and pleasant to the human ear. TTS engines have evolved significantly over the years, and today's high-performance engines offer an array of features and customization options.

TTS engines have a multitude of use cases, making them indispensable in various domains:

• Accessibility Features: TTS engines are integrated into operating systems like Android and Windows to provide speech output for individuals with visual impairments or reading difficulties.
• Content Creation: TTS engines are invaluable for content creators looking to convert text-based content, such as articles and blogs, into audio formats for audiobooks and podcasts.
• Automation: Businesses use TTS engines to automate customer experiences, such as generating voice prompts for customer support calls and notifications.
• Language Support: TTS engines support multiple languages, allowing users to access content in their preferred language.
• Real-Time Speech Synthesis: Some TTS engines, like Speechify, offer real-time speech synthesis, enabling users to listen to text as they type, making it a powerful productivity tool.
• Audiobooks: TTS engines are used in audiobook production, where they provide narration for books, improving accessibility for readers.

Speechify's TTS engine represents the cutting-edge of text-to-speech technology. It's a versatile and powerful tool that offers seamless integration through APIs, making it the next generation of TTS solutions. Unlike many open-source alternatives, Speechify's engine boasts high-quality, natural-sounding voices across multiple languages, making playback an immersive experience. It integrates effortlessly with Microsoft Edge, ensuring a consistent and user-friendly experience. With the ability to fine-tune permissions and leverage SSML for enhanced control, Speechify's TTS engine caters to a diverse range of callers and use cases. It leverages vast datasets, and unlike espeak, it delivers top-notch results in real-time. Whether you're creating templates, working with XML, or using it as a part of a Saas or SDK solution, Speechify's TTS engine sets a new standard for text-to-speech technology. You can find it on Google Play or seamlessly integrate it with Google Translate and HTML-based applications. Speechify, with its TTS engine at the core, has earned its reputation as the best TTS platform, and for a myriad of compelling reasons:

High-Quality, Natural-Sounding Voices

Speechify boasts a wide range of natural-sounding TTS voices in multiple languages, offering a superior listening experience. Whether it's English, Spanish, Russian, or Portuguese, Speechify's TTS voices sound remarkably lifelike.

Real-Time Speech Synthesis

Speechify's real-time speech synthesis feature is a game-changer for productivity. Users can listen to text as they type, ensuring error-free content and efficient editing. This functionality is unparalleled and transforms the user experience.

Seamless Integration

Speechify seamlessly integrates with various applications and platforms, including Google Docs, Chrome, and Amazon Polly. This integration ensures users can leverage Speechify's TTS engine wherever they work or consume content.

Diverse Use Cases

Speechify caters to a wide range of use cases, from audiobook production and content creation to accessibility features and customer experience automation. Its versatility makes it a valuable tool for individuals, businesses, and content creators alike.

Machine Learning and High Performance

Speechify's TTS engine harnesses the power of machine learning to ensure high performance and exceptional speech synthesis quality. This translates to a smoother, more natural experience for users.

Tutorials and Customer Support

Speechify offers comprehensive tutorials and customer support to guide users in maximizing the platform's capabilities. Whether you're new to TTS or an experienced user, Speechify ensures you make the most of its features.

Competitive Pricing

Speechify provides flexible pricing options, including free and premium plans, making it accessible to a wide range of users. The transparent pricing structure ensures users get the best value for their investment.

Natural-Sounding TTS Voices

Speechify's TTS engine focuses on delivering human-like voices with natural cadence and intonation. This attention to detail enhances the listening experience, making content more engaging and enjoyable.

While other TTS engines and platforms exist, Speechify stands out due to its unique combination of high-quality voices, real-time speech synthesis, seamless integration, and versatile functionality. It caters to a diverse user base and offers a user experience that is unparalleled in the world of TTS technology. Whether you're a content creator, a business automating customer experiences, or an individual looking to enhance accessibility, Speechify's TTS engine delivers exceptional results, setting a new standard in the TTS industry. In conclusion, the TTS engine is a transformative technology that has revolutionized content consumption and accessibility across various domains. While many TTS platforms are available, Speechify emerges as the best TTS platform, offering high-quality, natural-sounding voices, real-time speech synthesis, and seamless integration with a variety of applications. Its versatility, machine learning capabilities, and competitive pricing make it the ultimate choice for users seeking a top-notch TTS experience. Whether it's audiobook production, content creation, or improving accessibility, Speechify's TTS engine empowers users to engage with text in a whole new way.

How to convert text to speech with TTSMP3

Alternatives to Podcastle.ai for Podcast Creators

Cliff Weitzman

Cliff Weitzman is a dyslexia advocate and the CEO and founder of Speechify, the #1 text-to-speech app in the world, totaling over 100,000 5-star reviews and ranking first place in the App Store for the News & Magazines category. In 2017, Weitzman was named to the Forbes 30 under 30 list for his work making the internet more accessible to people with learning disabilities. Cliff Weitzman has been featured in EdSurge, Inc., PC Mag, Entrepreneur, Mashable, among other leading outlets.

SpeechTexter is a free multilingual speech-to-text application aimed at assisting you with transcription of notes, documents, books, reports or blog posts by using your voice. This app also features a customizable voice commands list, allowing users to add punctuation marks, frequently used phrases, and some app actions (undo, redo, make a new paragraph).

SpeechTexter is used daily by students, teachers, writers, bloggers around the world.

It will assist you in minimizing your writing efforts significantly.

Voice-to-text software is exceptionally valuable for people who have difficulty using their hands due to trauma, people with dyslexia or disabilities that limit the use of conventional input devices. Speech to text technology can also be used to improve accessibility for those with hearing impairments, as it can convert speech into text.

It can also be used as a tool for learning a proper pronunciation of words in the foreign language, in addition to helping a person develop fluency with their speaking skills.

Accuracy levels higher than 90% should be expected. It varies depending on the language and the speaker.

No download, installation or registration is required. Just click the microphone button and start dictating.

Speech to text technology is quickly becoming an essential tool for those looking to save time and increase their productivity.

Powerful real-time continuous speech recognition

Creation of text notes, emails, blog posts, reports and more.

Custom voice commands

More than 70 languages supported

SpeechTexter is using Google Speech recognition to convert the speech into text in real-time. This technology is supported by Chrome browser (for desktop) and some browsers on Android OS. Other browsers have not implemented speech recognition yet.

Note: iPhones and iPads are not supported

List of supported languages:

Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Bengali, Bosnian, Bulgarian, Burmese, Catalan, Chinese (Mandarin, Cantonese), Croatian, Czech, Danish, Dutch, English, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Kinyarwanda, Korean, Lao, Latvian, Lithuanian, Macedonian, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian Bokmål, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Serbian, Sinhala, Slovak, Slovenian, Southern Sotho, Spanish, Sundanese, Swahili, Swati, Swedish, Tamil, Telugu, Thai, Tsonga, Tswana, Turkish, Ukrainian, Urdu, Uzbek, Venda, Vietnamese, Xhosa, Zulu.

Instructions for web app on desktop (Windows, Mac, Linux OS)

Requirements: the latest version of the Google Chrome [↗] browser (other browsers are not supported).

1. Connect a high-quality microphone to your computer.

2. Make sure your microphone is set as the default recording device on your browser.

To go directly to microphone's settings paste the line below into Chrome's URL bar.

chrome://settings/content/microphone

To capture speech from video/audio content on the web or from a file stored on your device, select 'Stereo Mix' as the default audio input.

3. Select the language you would like to speak (Click the button on the top right corner).

4. Click the "microphone" button. Chrome browser will request your permission to access your microphone. Choose "allow".

5. You can start dictating!

Instructions for the web app on a mobile and for the android app

Requirements: - Google app [↗] installed on your Android device. - Any of the supported browsers if you choose to use the web app.

Supported android browsers (not a full list): Chrome browser (recommended), Edge, Opera, Brave, Vivaldi.

1. Tap the button with the language name (on a web app) or language code (on android app) on the top right corner to select your language.

2. Tap the microphone button. The SpeechTexter app will ask for permission to record audio. Choose 'allow' to enable microphone access.

3. You can start dictating!

Common problems on a desktop (Windows, Mac, Linux OS)

Error: 'speechtexter cannot access your microphone'..

Please give permission to access your microphone.

Click on the "padlock" icon next to the URL bar, find the "microphone" option, and choose "allow".

Error: 'No speech was detected. Please try again'.

If you get this error while you are speaking, make sure your microphone is set as the default recording device on your browser [see step 2].

If you're using a headset, make sure the mute switch on the cord is off.

Error: 'Network error'

The internet connection is poor. Please try again later.

The result won't transfer to the "editor".

The result confidence is not high enough or there is a background noise. An accumulation of long text in the buffer can also make the engine stop responding, please make some pauses in the speech.

The results are wrong.

Please speak loudly and clearly. Speaking clearly and consistently will help the software accurately recognize your words.

Reduce background noise. Background noise from fans, air conditioners, refrigerators, etc. can drop the accuracy significantly. Try to reduce background noise as much as possible.

Speak directly into the microphone. Speaking directly into the microphone enhances the accuracy of the software. Avoid speaking too far away from the microphone.

Speak in complete sentences. Speaking in complete sentences will help the software better recognize the context of your words.

Can I upload an audio file and get the transcription?

No, this feature is not available.

How do I transcribe an audio (video) file on my PC or from the web?

Playback your file in any player and hit the 'mic' button on the SpeechTexter website to start capturing the speech. For better results select "Stereo Mix" as the default recording device on your browser, if you are accessing SpeechTexter and the file from the same device.

I don't see the "Stereo mix" option (Windows OS)

"Stereo Mix" might be hidden or it's not supported by your system. If you are a Windows user go to 'Control panel' → Hardware and Sound → Sound → 'Recording' tab. Right-click on a blank area in the pane and make sure both "View Disabled Devices" and "View Disconnected Devices" options are checked. If "Stereo Mix" appears, you can enable it by right clicking on it and choosing 'enable'. If "Stereo Mix" hasn't appeared, it means it's not supported by your system. You can try using a third-party program such as "Virtual Audio Cable" or "VB-Audio Virtual Cable" to create a virtual audio device that includes "Stereo Mix" functionality.

How to use the voice commands list?

The voice commands list allows you to insert the punctuation, some text, or run some preset functions using only your voice. On the first column you enter your voice command. On the second column you enter a punctuation mark or a function. Voice commands are case-sensitive. Available functions: #newparagraph (add a new paragraph), #undo (undo the last change), #redo (redo the last change)

To use the function above make a pause in your speech until all previous dictated speech appears in your note, then say "insert a new paragraph" and wait for the command execution.

Found a mistake in the voice commands list or want to suggest an update? Follow the steps below:

• Navigate to the voice commands list [↑] on this website.
• Click on the edit button to update or add new punctuation marks you think other users might find useful in your language.
• Click on the "Export" button located above the voice commands list to save your list in JSON format to your device.

Next, send us your file as an attachment via email. You can find the email address at the bottom of the page. Feel free to include a brief description of the mistake or the updates you're suggesting in the email body.

Your contribution to the improvement of the services is appreciated.

Can I prevent my custom voice commands from disappearing after closing the browser?

SpeechTexter by default saves your data inside your browser's cache. If your browsers clears the cache your data will be deleted. However, you can export your custom voice commands to your device and import them when you need them by clicking the corresponding buttons above the list. SpeechTexter is using JSON format to store your voice commands. You can create a .txt file in this format on your device and then import it into SpeechTexter. An example of JSON format is shown below:

{ "period": ".", "full stop": ".", "question mark": "?", "new paragraph": "#newparagraph" }

I lost my dictated work after closing the browser.

SpeechTexter doesn't store any text that you dictate. Please use the "autosave" option or click the "download" button (recommended). The "autosave" option will try to store your work inside your browser's cache, where it will remain until you switch the "text autosave" option off, clear the cache manually, or if your browser clears the cache on exit.

Common problems on the Android app

I get the message: 'speech recognition is not available'..

'Google app' from Play store is required for SpeechTexter to work. download [↗]

Where does SpeechTexter store the saved files?

Version 1.5 and above stores the files in the internal memory.

Version 1.4.9 and below stores the files inside the "SpeechTexter" folder at the root directory of your device.

After updating the app from version 1.x.x to version 2.x.x my files have disappeared

As a result of recent updates, the Android operating system has implemented restrictions that prevent users from accessing folders within the Android root directory, including SpeechTexter's folder. However, your old files can still be imported manually by selecting the "import" button within the Speechtexter application.

Common problems on the mobile web app

Tap on the "padlock" icon next to the URL bar, find the "microphone" option and choose "allow".

• TERMS OF USE
• PRIVACY POLICY
• Play Store [↗]

copyright © 2014 - 2024 www.speechtexter.com . All Rights Reserved.

Top 11 Open Source Speech Recognition/Speech-to-Text Systems

Last Updated on: May 15, 2024

A speech-to-text (STT) system , or sometimes called automatic speech recognition (ASR) is as its name implies: A way of transforming the spoken words via sound into textual data that can be used later for any purpose.

Speech recognition technology is extremely useful. It can be used for a lot of applications such as the automation of transcription, writing books/texts using sound only, enabling complicated analysis on information using the generated textual files and a lot of other things.

In the past, the speech-to-text technology was dominated by proprietary software and libraries. Open source speech recognition alternatives didn’t exist or existed with extreme limitations and no community around.

This is changing, today there are a lot of open source speech-to-text tools and libraries that you can use right now.

Table of Contents:

What is a Speech Recognition Library/System?

What is an open source speech recognition library, what are the benefits of using open source speech recognition, 1. project deepspeech, 4. flashlight asr (formerly wav2letter++), 5. paddlespeech (formerly deepspeech2), 6. openseq2seq, 10. whisper, 11. styletts2, what is the best open source speech recognition system.

It is the software engine responsible for transforming voice to texts.

It is not meant to be used by end users. Developers will first have to adapt these libraries and use them to create computer programs that can enable speech recognition to users.

Some of them come with preloaded and trained dataset to recognize the given voices in one language and generate the corresponding texts, while others just give the engine without the dataset, and developers will have to build the training models themselves. This can be a complex task, similar to asking someone to do my online homework for me , as it requires a deep understanding of machine learning and data handling.

You can think of them as the underlying engines of speech recognition programs.

If you are an ordinary user looking for speech recognition, then none of these will be suitable for you, as they are meant for development use only.

The difference between proprietary speech recognition and open source speech recognition, is that the library used to process the voices should be licensed under one of the known open source licenses, such as GPL, MIT and others.

Microsoft and IBM for example have their own speech recognition toolkits that they offer for developers, but they are not open source. Simply because they are not licensed under one of the open source licenses in the market.

Mainly, you get few or no restrictions at all on the commercial usage for your application, as the open source speech recognition libraries will allow you to use them for whatever use case you may need.

Also, most – if not all – open source speech recognition toolkits in the market are also free of charge, saving you tons of money instead of using the proprietary ones.

The benefits of using open source speech recognition toolkits are indeed too many to be summarized in one article.

Top Open Source Speech Recognition Systems

In our article we’ll see a couple of them, what are their pros and cons and when they should be used.

This project is made by Mozilla, the organization behind the Firefox browser.

It’s a 100% free and open source speech-to-text library that also implies the machine learning technology using TensorFlow framework to fulfill its mission. In other words, you can use it to build training models by yourself to enhance the underlying speech-to-text technology and get better results, or even to bring it to other languages if you want.

You can also easily integrate it to your other machine learning projects that you are having on TensorFlow. Sadly it sounds like the project is currently only supporting English by default. It’s also available in many languages such as Python (3.6).

However, after the recent Mozilla restructure, the future of the project is unknown, as it may be shut down (or not) depending on what they are going to decide .

You may visit its Project DeepSpeech homepage to learn more.

Kaldi is an open source speech recognition software written in C++, and is released under the Apache public license.

It works on Windows, macOS and Linux. Its development started back in 2009. Kaldi’s main features over some other speech recognition software is that it’s extendable and modular: The community is providing tons of 3rd-party modules that you can use for your tasks.

Kaldi also supports deep neural networks, and offers an excellent documentation on its website . While the code is mainly written in C++, it’s “wrapped” by Bash and Python scripts.

So if you are looking just for the basic usage of converting speech to text, then you’ll find it easy to accomplish that via either Python or Bash. You may also wish to check Kaldi Active Grammar , which is a Python pre-built engine with English trained models already ready for usage.

Learn more about Kaldi speech recognition from its official website .

Probably one of the oldest speech recognition software ever, as its development started in 1991 at the University of Kyoto, and then its ownership was transferred to as an independent project in 2005. A lot of open source applications use it as their engine (Think of KDE Simon).

Julius main features include its ability to perform real-time STT processes, low memory usage (Less than 64MB for 20000 words), ability to produce N-best/Word-graph output, ability to work as a server unit and a lot more.

This software was mainly built for academic and research purposes. It is written in C, and works on Linux, Windows, macOS and even Android (on smartphones). Currently it supports both English and Japanese languages only.

The software is probably available to install easily using your Linux distribution’s repository; Just search for julius package in your package manager.

You can access Julius source code from GitHub.

If you are looking for something modern, then this one can be included.

Flashlight ASR is an open source speech recognition software that was released by Facebook’s AI Research Team. The code is a C++ code released under the MIT license.

Facebook was describing its library as “the fastest state-of-the-art speech recognition system available” up to 2018.

The concepts on which this tool is built makes it optimized for performance by default. Facebook’s machine learning library Flashlight is used as the underlying core of Flashlight ASR. The software requires that you first build a training model for the language you desire before becoming able to run the speech recognition process.

No pre-built support of any language (including English) is available. It’s just a machine-learning-driven tool to convert speech to text.

You can learn more about it from the following link .

Researchers at the Chinese giant Baidu are also working on their own speech recognition toolkit, called PaddleSpeech.

The speech toolkit is built on the PaddlePaddle deep learning framework, and provides many features such as:

• Speech-to-Text support.
• Text-to-Speech support.
• State-of-the-art performance in audio transcription, it even won the  NAACL2022 Best Demo Award ,
• Support for many large language models (LLMs), mainly for English and Chinese languages.

The engine can be trained on any model and for any language you desire.

PaddleSpeech ‘s source code is written in Python, so it should be easy for you to get familiar with it if that’s the language you use.

Developed by NVIDIA for sequence-to-sequence models training.

While it can be used for way more than just speech recognition, it is a good engine nonetheless for this use case. You can either build your own training models for it, or use models which are shipped by default. It supports parallel processing using multiple GPUs/Multiple CPUs, besides a heavy support for some NVIDIA technologies like CUDA and its strong graphics cards.

As of 2021 the project is archived; it can still be used but looks like it is no longer under active development.

Check its speech recognition documentation page for more information, or you may visit its official source code page .

One of the newest open source speech recognition systems, as its development just started in 2020.

Unlike other systems in this list, Vosk is quite ready to use after installation, as it supports 10 languages (English, German, French, Turkish…) with portable 50MB-sized models already available for users (There are other larger models up to 1.4GB if you need).

It also works on Raspberry Pi, iOS and android devices, and provides a streaming API which allows you to connect to it to do your speech recognition tasks online. Vosk has bindings for Java, Python, JavaScript, C# and NodeJS.

Learn more about Vosk from its official website .

An end-to-end speech recognition engine which implements ASR.

Written in Python and licensed under the Apache 2.0 license. Supports unsupervised pre-training and multi-GPUs training either on same or multiple machines. Built on the top of TensorFlow.

Has a large model available for both English and Chinese languages.

Visit Athena source code .

Written in Python on the top of PyTorch.

Also supports end-to-end ASR. It follows Kaldi style for data processing, so it would be easier to migrate from it to ESPnet. The main marketing point for ESPnet is the state-of-art performance it gives in many benchmarks, and its support for other language processing tasks such as speech-to-text (STT), machine translation (MT) and speech translation (ST).

Licensed under the Apache 2.0 license.

You can access ESPnet from the following link .

The newest speech recognition toolkit in the family, developed by the famous OpenAI company (the same company behind ChatGPT ).

The main marketing point for Whisper is that it does not specialize in a set of training datasets for specific languages only; instead, it can be used with any suitable model and for any language. It was trained on 680 thousand hours of audio files, one third of which were non-English datasets.

It supports speech-to-text, text-to-speech, speech translation. And the company claims that its toolkit has 50% less errors in the output compared to other toolkit in the market.

Learn more about Whisper from its official website .

The newest speech recognition library on the list, which was just released in the middle of November, 2023. It employs diffusion techniques with large speech language models (SLMs) training in order to achieve more advanced results than other models.

The makers of the model published it along with a research paper, where they make the following claim about their work:

This work achieves the first human-level TTS synthesis on both single and multispeaker datasets, showcasing the potential of style diffusion and adversarial training with large SLMs.

It is written in Python, and has some Jupyter notebooks shipped with it to demonstrate how to use it. The model is licensed under the MIT license.

There is an online demo where you can see different benchmarks of the model: https://styletts2.github.io/

If you are building a small application that you want to be portable everywhere, then Vosk is your best option, as it is written in Python and works on iOS, android and Raspberry pi too, and supports up to 10 languages. It also provides a huge training dataset if you shall need it, and a smaller one for portable applications.

If, however, you want to train and build your own models for much complex tasks, then any of PaddleSpeech, Whisper and Athena should be more than enough for your needs, as they are the most modern state-of-the-art toolkits.

As for Mozilla’s DeepSpeech , it lacks a lot of features behind its other competitors in this list, and isn’t really cited a lot in speech recognition academic research like the others. And its future is concerning after the recent Mozilla restructure, so one would want to stay away from it for now.

Traditionally, Julius and Kaldi are also very much cited in the academic literature.

Alternatively, you may try these open source speech recognition libraries to see how they work for you in your use case.

The speech recognition category is starting to become mainly driven by open source technologies, a situation that seemed to be very far-fetched a few years ago.

The current open source speech recognition software are very modern and bleeding-edge, and one can use them to fulfill any purpose instead of depending on Microsoft’s or IBM’s toolkits.

If you have any other recommendations for this list, or comments in general, we’d love to hear them below!

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With a B.Sc and M.Sc in Computer Science & Engineering, Hanny brings more than a decade of experience with Linux and open-source software. He has developed Linux distributions, desktop programs, web applications and much more. All of which attracted tens of thousands of users over many years. He additionally maintains other open-source related platforms to promote it in his local communities.

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Originally published on August 23, 2020, Last Updated on May 15, 2024 by M.Hanny Sabbagh

#1 Text To Speech (TTS) Reader Online

Proudly serving millions of users since 2015

Type or upload any text, file, website & book for listening online, proofreading, reading-along or generating professional mp3 voice-overs.

I need to >

Play Text Out Loud

Reads out loud plain text, files, e-books and websites. Remembers text & caret position, so you can come back to listening later, unlimited length, recording and more.

Create Humanlike Voiceovers

The simplest most robust & affordable AI voice-over generating tool online. Mix voices, languages & speeds. Listen before recording. Unlimited!

Additional Text-To-Speech Solutions

Turns your articles, PDFs, emails, etc. into podcasts, so you can listen to it on your own podcast player when convenient, with all the advantages that come with your podcast app.

SpeechNinja says what you type in real time. It enables people with speech difficulties to speak out loud using synthesized voice (AAC) and more.

Battle tested for years, serving millions of users, especially good for very long texts.

Need to read a webpage? Simply paste its URL here & click play. Leave empty to read about the Beatles 🎸

Books & Stories

Listen to some of the best stories ever written. We have them right here. Want to upload your own? Use the main player to upload epub files.

Simply paste any URL (link to a page) and it will import & read it out loud.

Chrome Extension

Reads out loud webpages, directly from within the page.

TTSReader for mobile - iOS or Android. Includes exporting audio to mp3 files.

NEW 🚀 - TTS Plugin

Make your own website speak your content - with a single line of code. Hassle free.

TTSReader Premium

Support our development team & enjoy ad-free better experience. Commercial users, publishers are required a premium license.

TTSReader reads out loud texts, webpages, pdfs & ebooks with natural sounding voices. Works out of the box. No need to download or install. No sign in required. Simply click 'play' and enjoy listening right in your browser. TTSReader remembers your text and position between sessions, so you can continue listening right where you left. Recording the generated speech is supported as well. Works offline, so you can use it at home, in the office, on the go, driving or taking a walk. Listening to textual content using TTSReader enables multitasking, reading on the go, improved comprehension and more. With support for multiple languages, it can be used for unlimited use cases .

Get Started for Free

Main Use Cases

Listen to great content.

Most of the world's content is in textual form. Being able to listen to it - is huge! In that sense, TTSReader has a huge advantage over podcasts. You choose your content - out of an infinite variety - that includes humanity's entire knowledge and art richness. Listen to lectures, to PDF files. Paste or upload any text from anywhere, edit it if needed, and listen to it anywhere and anytime.

Proofreading

One of the best ways to catch errors in your writing is to listen to it being read aloud. By using TTSReader for proofreading, you can catch errors that you might have missed while reading silently, allowing you to improve the quality and accuracy of your written content. Errors can be in sentence structure, punctuation, and grammar, but also in your essay's structure, order and content.

Listen to web pages

TTSReader can be used to read out loud webpages in two different ways. 1. Using the regular player - paste the URL and click play. The website's content will be imported into the player. (2) Using our Chrome extension to listen to pages without leaving the page . Listening to web pages with TTSReader can provide a more accessible, convenient, and efficient way of consuming online content.

Turn ebooks into audiobooks

Upload any ebook file of epub format - and TTSReader will read it out loud for you, effectively turning it into an audiobook alternative. You can find thousands of epub books for free, available for download on Project Gutenberg's site, which is an open library for free ebooks.

Read along for speed & comprehension

TTSReader enables read along by highlighting the sentence being read and automatically scrolling to keep it in view. This way you can follow with your own eyes - in parallel to listening to it. This can boost reading speed and improve comprehension.

Generate audio files from text

TTSReader enables exporting the synthesized speech with a single click. This is available currently only on Windows and requires TTSReader’s premium . Adhering to the commercial terms some of the voices may be used commercially for publishing, such as narrating videos.

Accessibility, dyslexia, etc.

For individuals with visual impairments or reading difficulties, listening to textual content, lectures, articles & web pages can be an essential tool for accessing & comprehending information.

Language learning

TTSReader can read out text in multiple languages, providing learners with listening as well as speaking practice. By listening to the text being read aloud, learners can improve their comprehension skills and pronunciation.

Kids - stories & learning

Kids love stories! And if you can read them stories - it's definitely the best! But, if you can't, let TTSReader read them stories for you. Set the right voice and speed, that is appropriate for their comprehension level. For kids who are at the age of learning to read - this can also be an effective tool to strengthen that skill, as it highlights every sentence being read.

Main Features

Ttsreader is a free text to speech reader that supports all modern browsers, including chrome, firefox and safari..

Includes multiple languages and accents. If on Chrome - you will get access to Google's voices as well. Super easy to use - no download, no login required. Here are some more features

Fun, Online, Free. Listen to great content

Drag, drop & play (or directly copy text & play). That’s it. No downloads. No logins. No passwords. No fuss. Simply fun to use and listen to great content. Great for listening in the background. Great for proof-reading. Great for kids and more. Learn more, including a YouTube we made, here .

Multilingual, Natural Voices

We facilitate high-quality natural-sounding voices from different sources. There are male & female voices, in different accents and different languages. Choose the voice you like, insert text, click play to generate the synthesized speech and enjoy listening.

Exit, Come Back & Play from Where You Stopped

TTSReader remembers the article and last position when paused, even if you close the browser. This way, you can come back to listening right where you previously left. Works on Chrome & Safari on mobile too. Ideal for listening to articles.

Vs. Recorded Podcasts

In many aspects, synthesized speech has advantages over recorded podcasts. Here are some: First of all - you have unlimited - free - content. That includes high-quality articles and books, that are not available on podcasts. Second - it’s free. Third - it uses almost no data - so it’s available offline too, and you save money. If you like listening on the go, as while driving or walking - get our free Android Text Reader App .

Read PDF Files, Texts & Websites

TTSReader extracts the text from pdf files, and reads it out loud. Also useful for simply copying text from pdf to anywhere. In addition, it highlights the text currently being read - so you can follow with your eyes. If you specifically want to listen to websites - such as blogs, news, wiki - you should get our free extension for Chrome

Export Speech to Audio Files

TTSReader enables exporting the synthesized speech to mp3 audio files. This is available currently only on Windows, and requires ttsreader’s premium .

Pricing & Plans

• Online text to speech player
• Chrome extension for reading webpages
• Premium TTSReader.com
• Premium Chrome extension
• Better support from the development team

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Dictation & Transcription

Type with your voice for free, or automatically transcribe audio & video recordings

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Turns your device into multiple push-buttons interactive games

Animals, numbers, colors, counting, letters, objects and more. Different levels. Multilingual. No ads. Made by parents, for our own kids.

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The 5 Best Open Source Speech Recognition Engines & APIs

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Rev › Blog › Resources › Other Resources › Speech-to-Text APIs › The 5 Best Open Source Speech Recognition Engines & APIs

In this article, we provide a breakdown of five of the best free-to-use open source speech recognition services along with details on how you can get started.

Mozilla DeepSpeech

DeepSpeech is a Github project created by Mozilla, the famous open source organization which brought you the Firefox web browser. Their model is based on the Baidu Deep Speech research paper and is implemented using Tensorflow (which we’ll talk about later).

Pros of Mozilla DeepSpeech

• They provide a pre-trained English model, which means you can use it without sourcing your own data. However, if you do have your own data, you can train your own model, or take their pre-trained model and use transfer learning to fine tune it on your own data.
• DeepSpeech is a code-native solution, not an API . That means you can tweak it according to your own specifications, providing the highest level of customization.
• DeepSpeech also provides wrappers into the model in a number of different programming languages, including Python, Java, Javascript, C, and the .NET framework. It can also be compiled onto a Raspberry Pi device which is great if you’re looking to target that platform for applications.

Cons of Mozilla DeepSpeech

• Due to some layoffs and changes in organization priorities, Mozilla is winding down development on DeepSpeech and shifting its focus towards applications of the tech. This could mean much less support when bugs arise in the software and issues need to be addressed.
• The fact that DeepSpeech is provided solely as a Git repo means that it’s very bare bones. In order to integrate it into a larger application, your company’s developers would need to build an API around its inference methods and generate other pieces of utility code for handling various aspects of interfacing with the model.

2. Wav2Letter++

The Wav2Letter++ speech engine was created in December 2018 by the team at Facebook AI Research. They advertise it as the first speech recognition engine written entirely in C++ and among the fastest ever.

Pros of Wav2Letter++

• It is the first ASR system which utilizes only convolutional layers , not recurrent ones. Recurrent layers are common to nearly every modern speech recognition engine as they are particularly useful for language modeling and other tasks which contain long-range dependencies.
• Within Wav2Letter++ the code allows you to either train your own model or use one of their pretrained models. They also have recipes for matching results from various research papers, so you can mix and match components in order to fit your desired results and application.

Cons of Wav2Letter++

• The downsides of Wav2Letter++ are much the same as with DeepSpeech. While you get a very fast and powerful model, this power comes with a lot of complexity. You’ll need to have deep coding and infrastructure knowledge in order to be able to get things set up and working on your system.

Kaldi is an open-source speech recognition engine written in C++, which is a bit older and more mature than some of the others in this article. This maturity has both benefits and drawbacks.

Pros of Kaldi

• On the one hand, Kaldi is not really focused on deep learning, so you won’t see many of those models here. They do have a few, but deep learning is not the project’s bread and butter. Instead, it is focused more on classical speech recognition models such as HMMs, FSTs and Gaussian Mixture Models.
• Kaldi methods are very lightweight, fast, and portable.
• The code has been around a long time, so you can be assured that it’s very thoroughly tested and reliable.
• They have good support including helpful forums, mailing lists, and Github issues trackers which are frequented by the project developers.
• Kaldi can be compiled to work on some alternative devices such as Android.

Cons of Kaldi

• Because Kaldi is not focused on deep learning, you are unlikely to get the same accuracy that you would using a deep learning method.

4. Open Seq2Seq

Open Seq2Seq is an open-source project created at Nvidia. It is a bit more general in that it focuses on any type of seq2seq model, including those used for tasks such as machine translation, language modeling, and image classification. However, it also has a robust subset of models dedicated to speech recognition.

The project is somewhat more up-to-date than Mozilla’s DeepSpeech in that it supports three different speech recognition models: Jasper DR 10×5, Baidu’s DeepSpeech2, and Facebook’s Wav2Letter++.

Pros of Seq2Seq

• The best of these models, Jasper DR 10×5, has a word error rate of just 3.61%.
• Note that the models do take a fair amount of computational power to train. They estimate that training DeepSpeech2 should take about a day using a GPU with 12 GB of memory.

Cons of Seq2Seq

• One negative with Open Seq2Seq is that the project has been marked as archived on Github, meaning that development has most likely stopped. Thus, any errors that arise in the code will be up to users to solve individually as bug fixes are not being merged into the main codebase.

5. Tensorflow ASR

Tensorflow ASR is a speech recognition project on Github that implements a variety of speech recognition models using Tensorflow. While it is not as well known as the other projects, it seems more up to date with its most recent release occurring in just May of 2021.

The author describes it as “almost state of the art” speech recognition and implements many recent models including DeepSpeech 2, Conformer Transducer, Context Net, and Jasper. The models can be deployed using TFLite and they will likely integrate nicely into any existing machine-learning system which uses Tensorflow. It also contains pretrained models for a couple of foreign languages including Vietnamese and German.

What Makes Rev AI Different

While open-source speech recognition systems give you access to great models for free, they also undeniably make things complicated. This is simply because speech recognition is complicated. Even when using an open-source pre-trained model, it takes a lot of work to get the model fine-tuned on your data, hosted on a server, and to write APIs to interface with it. Then you have to worry about keeping the system running smoothly and handling bugs and crashes when they inevitably do occur.

The great thing about using a paid provider such as Rev is that they handle all those headaches for you. You get a system with guaranteed 99.9+% uptime with a callable API that you can easily hook your product into. In the unlikely event that something does go wrong, you also get direct access to Rev’s development team and fantastic client support.

Another advantage of Rev is that it’s the most accurate speech recognition engine in the world. Their system has been benchmarked against the ones provided by all the other major industry players such as Amazon, Google, Microsoft, etc. Rev comes out on top every single time with the lowest average word error rate across multiple, real-world datasets.

Finally, when you use a third-party solution such as Rev, you can get up and running immediately. You don’t have to wait around to hire a development team, to train models, or to get everything hosted on a server. Using a few simple API calls you can hook your frontend right into Rev’s ASR system and be ready to go that very same day. This ultimately saves you money and likely more than recoups the low cost that Rev charges.

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Simple and free Text-to-Speech (TTS) engine that reads for you any text on your screen with high-quality voices powered by AI models.

MattePalte/Verbify-TTS

Folders and files, repository files navigation, verbify-tts.

Verbify-TTS is a simple Text-to-Speech (TTS) engine that reads for you any text on your screen with high-quality voices powered by AI models. It is free and you can use it for unlimited time (Open Source MIT LICENSE ).

The main features of Verbify-TTS are:

• Compatible with any desktop Application : Verbify-TTS is compatible with any desktop application where you can select text with your mouse.
• High-quality voices powered by AI : The voices of Verbify-TTS are powered by AI and they learned on thousands of audio and text data.
• Free and unlimited usage : You can use the voices of Verbify-TTS for free and for unlimited time, all you need is to install Verbify-TTS on your system.
• Registration-free : You don't need to register to use Verbify-TTS, no subscription needed.
• Private data : the data the application reads stay only on your device. There is no tracking or monitoring whatsoever. Everything is under your full control.
• Customizable : convert each special or domain-specific word into another word combination. We have some example such as "e.g." pronounced as "for example". Modify the simple idioms.csv file and add yours.

Some popular use cases of Verbify-TTS are:

• read your pdf research papers or your favorite book. Verbify-TTS is compatible with any document reader or ebook reader (e.g. Adobe Reader, Okular, Calibre, and others)
• any desktop app , such as word processors (e.g. Microsoft Word, LibreOffice Writer, etc.)
• any web browser and web page (e.g. Google Chrome, Mozilla Firefox, Safari, etc.)
• anywhere in your device where you can select and copy text from with your mouse.

Simple Installation Guide for Everyone

This guide is designed also for non-tech people.

If you are more a visual guy, you can also look at the VIDEO TUTORIAL I created for you.

To install the Verbify-TTS on your system you need to follow few simple steps. Note that, for those of you unfamiliar with technology we have some tips to help you.

INSTALL PYTHON 3 . Python is a software (or better called programming language) which Verbify-TTS uses to run, thus your computer should have it installed. We need version 3.8 or higher. Tip : to install Python on your system follow these step-by-step videos: video for Windows or video for Mac .

DOWNLOAD VERBIFY FILES. Download the content of this Github repository in your device, one way to do that is using your command git clone . Warning : place these files in a definitive location since moving them after the installation is completed will likely compromise the functionality of the application. Tip : If you are not familiar with Git, watch this video to learn how to download the content of this repository.

OPEN THE TERMINAL. Open the terminal at the main folder of the repository, namely the folder containing the README.md file. Tip : If you are not familiar with terminal, watch this video (Mac OS) or this video (Windows) to learn how to open the terminal in the specified folder.

RUN THE INSTALLATION PROGRAM.

• LINUX case (for the moment the only tested): Type the following in your terminal and click enter: ./INSTALL_LINUX.sh The installer will stop when all the packages have been downloaded and it will ask you if you want to start the service at each startup of the computer, type y and click enter, then insert the password of the root user or admin.
• WINDOWS case: Type the following in your terminal and click enter: . \I NSTALL_WINDOWS.bat The installer will stop when all the packages have been downloaded and it will give you information on the specific shortcuts for Windows.
• MAC OS case (open an issue in this repository if interested, or upvote an existing one.)

SET YOUR KEY SHORTCUT. At the end of the installation, the terminal will give you two commands which you have to connect to the key bindings of your window manager system so that you can use these shortcut anywhere.

• Linux : This is dependent on the specific window manager you are using, so you have to Google a bit on how to do that in your specific case. I tested on XFCE you can find it here . We recommend ALT + ESC to start the reading and ALT + END to end the reading, but you can choose whatever you want.
• Windows : You need to install AutoHotKey which you can find here . Then restart the system and it should work. For Window the default shortcut is CTRL + ESC to start the reading and CTRL + END to end the reading.

RESTART AND READY TO GO! Once you connected the key bindings, you have to restart the system and Verbify-TTS will be ready to use. Select any text and press the key binding you have set (e.g. ALT + ESC) to read it out loud.

LEAVE A STAR. If you like Verbify-TTS please drop a star at the top right of this page to support the project and thank the developers.

Change reading speed : change the constant reading_speed in the configuration file at configuration/config.yaml . A value of 1.45 is the default reading speed which I use to be more productive, but 1 is the value to give a more natural voice, just a bit slower.

Change shortcuts on Windows : to change the shortcut, you can change the base_read.ahk and base_stop.ahk files in the configuration folder before running the installer. You can also change the shortcut after the installation by editing the base_read.ahk and base_stop.ahk files in the startup folder: C:\Users\User\AppData\Roaming\Microsoft\Windows\Start Menu\Programs\Startup .

Note: the system has been tested with:

• Linux (Ubuntu 20.04)
• Python 3.8.8
• XFCE window manager for shortcut (Xubuntu 20.04) Feel free to open an issue in this repository if you are having troubles with the installation on another operative system.

Other configurations are not guaranteed to work, please create an issue so that we can help you.

Acknowledgements

Thanks to TensorSpeech for having trained and shared under Apache License 2 the AI models used under-the-hood by Verbify-TTS: tensorspeech/tts-fastspeech2-ljspeech-en and tensorspeech/tts-mb_melgan-ljspeech-en .

Beside leaving a start to Verbify-TTS, please drop a star also on their repository since they were vital for the success of Verbify-TTS.

Thanks to the researchers who developed the AI models:

• FastSpeech 2: Fast and High-Quality End-to-End Text to Speech , Yi Ren and Chenxu Hu and Xu Tan and Tao Qin and Sheng Zhao and Zhou Zhao and Tie-Yan Liu.
• Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech , Geng Yang and Shan Yang and Kai Liu and Peng Fang and Wei Chen and Lei Xie.
• Python 50.7%
• Shell 23.8%
• Batchfile 22.1%
• AutoHotkey 3.4%

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An embedded Text-to-Speech engine geared for automotive, mobile and other electronic applications. It provides more natural-sounding speech in a variety of applications and technologies.

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A comprehensive, user-friendly suite of tools that allows users to prototype and optimize speech output applications by easily creating optimization data such as user text rules, user dictionaries and prompts.

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Frequently asked questions, how do i make my own ai voice.

To create your own AI voice at ElevenLabs, you can use VoiceLab. Voice Design allows you to customize the speaker's identityfor unique voices in your scripts, while Voice Cloning mimics real voices. This ensures variety and exclusivity in your generated voices, as they are entirely artificial and not linked to real people.

How much does using ElevenLabs AI voice generator cost?

ElevenLabs provides a range of AI voice generation plans suitable for various needs. Starting with a Free Plan, which includes 10,000 characters monthly, up to 3 custom voices, Voice Design, and speech generation in 29 languages. The Starter Plan is $5 per month, offering 30,000 characters and up to 10 custom voices. For more extensive needs, the Creator Plan at $22 per month provides 100,000 characters and up to 30 custom voices. The Pro Plan costs $99 per month with a substantial 500,000 characters and up to 160 custom voices. Larger businesses can opt for the Scale Plan at $330 per month, which includes 2,000,000 characters and up to 660 custom voices. Lastly, the Enterprise Plan offers custom pricing for tailored quotas, PVC for any voice, priority rendering, and dedicated support. Each plan is crafted to support different levels of usage and customization requirements.

Can I use ElevenLabs AI voice generator for free?

Yes, you can use ElevenLabs prime AI voice generator for free with our Free Plan. It includes 10,000 characters per month, up to 3 custom voices, Voice Design, and speech generation in 29 languages.

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ElevenLabs offers the best and highest quality AI voice generator software online. Our AI voice generator uses advanced deep learning models to provide high-quality audio output, emotion mapping, and a wide range of vocal choices. It's perfect for content creators and writers looking to create captivating audio experiences.

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ElevenLabs' AI voice generator is ideal for a variety of users, including content creators on YouTube and TikTok, audiobook producers for Audible and Google Play Books, presenters using PowerPoint or Google Docs, businesses with IVR systems, and podcasters on Spotify or Apple Podcasts. These services provide a natural-sounding voice across different platforms, enhancing user engagement and accessibility.

How many languages does ElevenLabs support?

ElevenLabs supports speech synthesis in 29 languages, making your content accessible to a global audience. Supported languages include Chinese, English, Spanish, French, and many more.

What is an AI voice generator?

ElevenLabs' AI voice generator transforms text to spoken audio that sounds like a natural human voice, complete with realistic intonation and accents. It offers a wide range of voice options across various languages and dialects. Designed for ease of use, it caters to both individuals and businesses looking for customizable vocal outputs.

How do I use AI voice generators to turn text into audio?

Step 1 involves selecting a voice and adjusting settings to your liking. In Step 2, you input your text into the provided box, ensuring it's in one of the supported languages. For Step 3, you simply click 'Generate' to convert your text into audio, listen to the output, and make any necessary adjustments. After that, you can download the audio for use in your project.

What is text to speech?

Text to speech is a technology that converts written text into spoken audio. It is also known as speech synthesis or TTS. The technology has been around for decades, but recent advancements in deep learning have made it possible to generate high-quality, natural-sounding speech.

What is the best text to speech software?

ElevenLabs is the best text to speech software. We offer the most advanced AI voices, with the highest quality and most natural-sounding speech. Our platform is easy to use and offers a wide range of customization options.

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ElevenLabs offers a free plan which includes 10,000 characters per month. Our paid plans start at $1 for 30,000 characters per month.

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Using Amazon Polly on the AWS CLI

Amazon Polly is a managed service provided by AWS that makes it easy to synthesize speech from text. In this article, we will learn how to use Polly through the AWS CLI. We will learn how to use all the commands available in Polly along with some examples.

Using Polly with AWS CLI

Make sure you have the latest version of AWS CLI and configured your access keys before proceeding further.

• Security Updates: Newer versions of the AWS CLI often contain critical security patches that fix vulnerabilities. These vulnerabilities could potentially be exploited by malicious actors to gain unauthorized access to your AWS account or resources. Using an outdated version leaves your account exposed to these risks.
• New Features and Functionality: Amazon Polly and other AWS services are constantly evolving with new features and functionalities. The latest AWS CLI ensures you have access to the most recent options and commands for interacting with Polly.
• Bug Fixes: Bugs in the AWS CLI can cause unexpected behavior or errors when using Polly commands. The latest version likely has these bugs addressed, leading to a smoother and more reliable experience.
• Configured Access Keys:
• Authentication: Access keys are your credentials for interacting with AWS services like Polly. They act like a username and password, proving your identity and granting authorization to perform actions. Without configured access keys, you won’t be able to use any AWS CLI commands, including those for Polly.
• Security Best Practices: It’s recommended to use temporary, short-lived access keys for programmatic access (like the AWS CLI) instead of long-term credentials. Configuring access keys allows you to set permissions that limit what actions the CLI can perform on your account, minimizing potential damage in case of accidental misuse.

Finding Help with AWS Polly in the CLI

Use the help command to get a list of commands that are available in AWS Polly CLI.

To get help, for a specific command in Polly:

For example,

Synthesizing speech using AWS CLI commands

This command generates a file named hello.mp3 . In addition to the MP3 file, the operation sends the following output to the console.

The –voice-id is the voice that should be used in the audio file. There are many voices available in AWS Polly for each of the language. You can get a list of voice id using the aws polly synthesize-speech help command and look in the –voice-id section or the describe-voices command.

To generate a speech in another language use the –language-code option. This command produces audio in Indian English with the voice id as Aditi . You can get the list of the language codes with the help command.

Find the voice ids related to a specific language. This command prints all the available voices for Indian English.

describe-voices –language code en-IN output

AWS Polly has three kinds of text to speech engines: standard , neural and long-form . Use the –engine option to configure the engine used to produce speech. This command uses the neural engine with Kajal voice id to produce speech.

Not all voices supports the neural engine. If you use an unsupported voice id for neural engine then it will cause an error.

The synthesize-speech command has many options available that supports multiple languages, file formats, voices, engines, SSML etc. which can be found in the AWS documentation or aws polly synthesize-speech help command.

Speech synthesis tasks

A speech synthesis task is an asynchronous operation that allows you to create speech synthesis tasks. These are suitable for long texts which can take a while to produce the results. The generated audio files are stored in an S3 bucket. Once the task is created you will get a SpeechSynthesisTask object, which includes id of the task and other details. This object is available for 72 hours after starting the task.

This command starts a speech synthesis task that gets its input from the input.txt (input.txt should be in the same directory) file and stores the file in `my-s3-bucket`. (Make sure you have created a bucket and use that bucket name in –output-s3-bucket-name option.)

• TaskId : the id of the task you just created.
• TaskStatus : Current status of the task.
• OutputUri : Pathway of the output speech file.
• CreationTime : Timestamp for the time the synthesis task was started.
• RequestCharacters : Number of billable characters synthesized.
• OutputFormat : Format in which the output file will be encoded.
• TextType : Specifies whether the input text is plain text or SSML.
• VoiceId : Voice ID used for the synthesis.

To list all the speech synthesis tasks use the `list-speech-synthesis-tasks` command.

To get a specific speech synthesis task based on its TaskId use the `get-speech-synthesis-task` command.

Managing Lexicons

Pronunciation lexicons allows you to customize the pronunciation of words. For example, you can use lexicons pronounce AWS as Amazon Web Services. You can generate lexicons in an AWS region. Those lexicons are then specific to that region. You can manage lexicons using the `list-lexicons`, `put-lexicon`, `get-lexicon` and `delete-lexicon` commands.

Create a file named lexicon1.pls and add below text to it.

The <lexeme> tags describes the mapping between <grapheme> and <alias> . <graphene> describes the which text needs modified pronunciation and <alias> defines how it should be pronounced. In this example, AWS will be pronounced as Amazon Web Services in the synthesized speech when this lexicon is used during speech synthesis.

To add this lexeme use the put-lexicon command. The –name option is used to specify the name of the lexicon. You can use it to refer to it during speech synthesis.

Now generate speech using the lexicon.

Now AWS is synthesized as Amazon Web Services in speech.mp3

You can also include multiple lexeme in a single lexicon. For example,

If two lexemes have same grapheme then the synthesis engine uses the one that comes first.

You can even use multiple lexicons in a single command.

Here, lexicon1 and lexicon2 are two lexicons. If any grapheme in both of them are same, the ones in the first lexicon that is lexicon1 will be used.

List all the available lexicons using the list-lexicons command

Get a single lexicon by name using the get-lexicon command

Delete a lexicon using the `delete-lexicon` command

Using Amazon Polly on the AWS CLI – FAQs

How do i get help for aws polly cli.

You can use the aws polly help command to get a list of available commands. For help with a specific command, use aws polly COMMAND help.

What are the different types of text-to-speech engines in Polly?

Polly supports standard, neural, and long-form text-to-speech engines. You can specify the engine using the –engine option. Standard engine is the most basic and standard one. Neural engine is newer as compared to standard and sounds more natural as compared to it. long-form is suitable for large amounts of text.

What is a pronunciation lexicon in Amazon Polly?

A pronunciation lexicon allows you to customize the pronunciation of words. It maps specific words or phrases to their desired pronunciation.

Can I use SSML (Speech Synthesis Markup Language) with Polly?

Yes, you can use SSML to add markup to your text, such as specifying pauses, emphasis, or changing the speaking rate. Pass the SSML input to the synthesize-speech command using the –text-type ssml option.

Can I monitor the progress of a speech synthesis task?

Yes, you can use the get-speech-synthesis-task command with the task ID to retrieve details about the status and progress of a speech synthesis task.

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